ABSTRACT. Mergers and acquisitions (M&A) are undertaken to improve efficiencies or increase economies of scale and scope. Prices offered by buyers can depend on the personality of the buyer (acquiring firm) and seller (target firm). This paper tries to model these differences in prices offered for M&A deals using agent-based modeling and considers risk-averse – risk-taking behavior of buyers and optimistic – pessimistic behavior of sellers. Further, if we add to this the situation of a hostile takeover, it tends to complicate the pricing even more. This paper uses agent-based modeling to analyze a hostile takeover while considering the risk-averse – risk-taking behavior of the acquirer and optimistic – pessimistic behavior of the target firm. Results show that risk-taking buyer will pay higher prices and optimistic sellers will demand higher prices. As a result, sellers obtain the highest premium when these behavior traits exist. Also, sellers receive higher prices when the business cycle is improving, while, receiving the lowest premium during the business cycle trough or peak as buyers know that sellers have limited options during troughs and buyers are afraid of paying too much to merger or acquire a seller during business cycle peaks. Also, results show that as the hostile takeover characteristic of the acquirer increases, it puts downward pressure on the final price paid to purchase the target firm. This specifically comes under play when the target firm is less optimistic, the target firm’s shareholders will usually accept a lower price compared to when they are more optimistic and hold out for a better offer. pp. 55–67
JEL codes: G34; L11

Keywords: price; mergers and acquisitions (M&A); agent-based modeling

How to cite: Agarwal, Nipun, and Paul Kwan (2017), “Pricing Mergers & Acquisitions Using Agent-based Modeling,” Economics, Management, and Financial Markets 12(1): 55–67.

Received 28 February 2016 • Received in revised form 18 March 2016
Accepted 19 March 2016 • Available online 1 April 2016

doi:10.22381/EMFM12120173